To address this problem in Raw PC-DARTS, we apply maxpooling to each cell output to reduce the feature dimension by one-half. This simple, yet efficient solution helps the model to learn a more compact, high-level representation, without increasing the number of channels, thereby reducing computational complexity and demands upon GPU memory. An added benefit is that the same architecture depth and initial number of channels can be used for both architecture search as well as train from scratch stages. The so-called depth gap [1]}, [2]} is therefore avoided, where the searched operations may not fit the deeper network in the second stage due to the depth mismatch between architecture search and train from scratch stages. Thus, the cells used in Raw PC-DARTS are referred to as a normal cell and an expand cell. Both cells halve the input feature dimension, whereas only the expand cell doubles the number of channels. Expand cells are placed at the same network depth as reduction cells in the original DARTS approach.
